Simulating the Impact of Traffic Calming Strategies

This study assessed the impact of traffic calming measures to the speed, travel times and capacity of residential roadways. The study focused on two types of speed tables, speed humps and a raised crosswalk. A moving test vehicle equipped with GPS receivers that allowed calculation of speeds and determination of speed profiles at 1s intervals were used. Multi-regime model was used to provide the best fit using steady state equations; hence the corresponding speed-flow relationships were established for different calming scenarios. It was found that capacities of residential roadway segments due to presence of calming features ranged from 640 to 730 vph. However, the capacity varied with the spacing of the calming features in which spacing speed tables at 1050 ft apart caused a 23% reduction in capacity while 350-ft spacing reduced capacity by 32%. Analysis showed a linear decrease of capacity of approximately 20 vphpl, 37 vphpl and 34 vphpl when 17 ft wide speed tables were spaced at 350 ft, 700 ft, and 1050 ft apart respectively. For speed hump calming features, spacing humps at 350 ft reduced capacity by about 33% while a 700 ft spacing reduced capacity by 30%. The study concludes that speed tables are slightly better than speed humps in terms of preserving the roadway capacity. Also, traffic calming measures significantly reduce the speeds of vehicles, and it is best to keep spacing of 630 ft or less to achieve desirable crossing speeds of less or equal to 15 mph especially in a street with schools nearby. A microscopic simulation model was developed to replicate the driving behavior of traffic on urban road diets roads to analyze the influence of bus stops on traffic flow and safety. The impacts of safety were assessed using surrogate measures of safety (SSAM). The study found that presence of a bus stops for 10, 20 and 30 s dwell times have almost 9.5%, 12%, and 20% effect on traffic speed reductions when 300 veh/hr flow is considered. A comparison of reduction in speed of traffic on an 11 ft wide road lane of a road diet due to curbside stops and bus bays for a mean of 30s with a standard deviation of 5s dwell time case was conducted. Results showed that a bus stop bay with the stated bus dwell time causes an approximate 8% speed reduction to traffic at a flow level of about 1400 vph. Analysis of the trajectories from bust stop locations showed that at 0, 25, 50, 75, 100, 125, 150, and 175 feet from the intersection the number of conflicts is affected by the presence and location of a curbside stop on a segment with a road diet

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.Comment: Accepted by AAAI 2023, appendix included. 9 pages + 5 pages appendix, 12 figures, in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23), Feb 202

Guidelines for the Use of Synthetic Fluid Dust Control Palliatives on Unpaved Roads

The amount of small soil particles, dust, lost from typical unpaved roads to fugitive dust is staggering. A 1 km stretch of unpaved road can contribute over 2400 kg of dust to the atmosphere (4.2 ton/mile) in a typical 3-month summer season. Road managers typically manage dust from unpaved roads with various dust-control palliatives, which are effective for up to 1 year. Synthetic fluids are a relatively new category of dust-control palliatives. Unlike the more commonly used dust-control palliatives, such as salts, engineering guidelines do not exist for the application and maintenance of synthetic fluids on unpaved roads. To fill this void, we present through this document guidelines for road design and maintenance, palliative selection, application, and care of synthetic fluid-treated roadways.Midwest Industrial Supply United States Department of TransportationReport Documentation Page .............................................................................................. ii Disclaimer ......................................................................................................................... iii List of Figures .................................................................................................................... vi Executive Summary............................................................................................................. 1 CHAPTER 1.0 – Introduction............................................................................................... 4 CHAPTER 2.0 – Background.............................................................................................. 6 Measurements of the Effectiveness of Dust Palliatives .....................................................10 CHAPTER 3.0 – Guidelines .............................................................................................. 16 Road Design and Maintenance...........................................................................................16 Palliative Selection..............................................................................................................20 Application .........................................................................................................................22 Areas Requiring Special Attention......................................................................................26 Maintenance .......................................................................................................................27 CHAPTER 4.0 – Summary................................................................................................. 31 CHAPTER 5.0 – References.............................................................................................. 3

Simulation Of Vehicular Movement in VANET

In the recent years research in the field of vehicular ad-hoc network(VANET) is done extensively. VANET consist of large number of dynamically nodes which are vehicles over a area. Different types of technology and applications are being developed for the VANET . So this VANET technology and applications should be thoroughly checked before deployment in the real world environment. But to test technologies and applications in real world environment is not feasible it involves lot of danger and safety issues, different reports of the testing can’t also be generated so to overcome these limitation we need to carry out simulation of VANET in the computer environment i.e. we should do a computer simulation. Computer simulation is risk and danger free, we can generate different scenario (rural, urban, collision of vehicles) of the VANET using this. So computer simulation is very important in VANET research. Simulation of VANET is divided into two part a. Traffic simulation: Generation of traffic movement, Defining the mobility model for vehicle and creating traffic movement. b. Network simulation: Generating Inter communicating vehicle , Defining communication protocols. And both the simulation are connected in bi-directional coupling

A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Cellular Automata Models of Road Traffic

In this paper, we give an elaborate and understandable review of traffic cellular automata (TCA) models, which are a class of computationally efficient microscopic traffic flow models. TCA models arise from the physics discipline of statistical mechanics, having the goal of reproducing the correct macroscopic behaviour based on a minimal description of microscopic interactions. After giving an overview of cellular automata (CA) models, their background and physical setup, we introduce the mathematical notations, show how to perform measurements on a TCA model's lattice of cells, as well as how to convert these quantities into real-world units and vice versa. The majority of this paper then relays an extensive account of the behavioural aspects of several TCA models encountered in literature. Already, several reviews of TCA models exist, but none of them consider all the models exclusively from the behavioural point of view. In this respect, our overview fills this void, as it focusses on the behaviour of the TCA models, by means of time-space and phase-space diagrams, and histograms showing the distributions of vehicles' speeds, space, and time gaps. In the report, we subsequently give a concise overview of TCA models that are employed in a multi-lane setting, and some of the TCA models used to describe city traffic as a two-dimensional grid of cells, or as a road network with explicitly modelled intersections. The final part of the paper illustrates some of the more common analytical approximations to single-cell TCA models.Comment: Accepted for publication in "Physics Reports". A version of this paper with high-quality images can be found at: http://phdsven.dyns.cx (go to "Papers written"

Cooperative Intersection Crossing Over 5G

IEEE Autonomous driving is a safety critical application of sensing and decision-making technologies. Communication technologies extend the awareness capabilities of vehicles, beyond what is achievable with the on-board systems only. Nonetheless, issues typically related to wireless networking must be taken into account when designing safe and reliable autonomous systems. The aim of this work is to present a control algorithm and a communication paradigm over 5G networks for negotiating traffic junctions in urban areas. The proposed control framework has been shown to converge in a finite time and the supporting communication software has been designed with the objective of minimizing communication delays. At the same time, the underlying network guarantees reliability of the communication. The proposed framework has been successfully deployed and tested, in partnership with Ericsson AB, at the AstaZero proving ground in Goteborg, Sweden. In our experiments, three heterogeneous autonomous vehicles successfully drove through a 4-way intersection of 235 square meters in an urban scenario

A Computationally Efficient Bi-level Coordination Framework for CAVs at Unsignalized Intersections

In this paper, we investigate cooperative vehicle coordination for connected and automated vehicles (CAVs) at unsignalized intersections. To support high traffic throughput while reducing computational complexity, we present a novel collision region model and decompose the optimal coordination problem into two sub-problems: \textit{centralized} priority scheduling and \textit{distributed} trajectory planning. Then, we propose a bi-level coordination framework which includes: (i) a Monte Carlo Tree Search (MCTS)-based high-level priority scheduler aims to find high-quality passing orders to maximize traffic throughput, and (ii) a priority-based low-level trajectory planner that generates optimal collision-free control inputs. Simulation results demonstrate that our bi-level strategy achieves near-optimal coordination performance, comparable to state-of-the-art centralized strategies, and significantly outperform the traffic signal control systems in terms of traffic throughput. Moreover, our approach exhibits good scalability, with computational complexity scaling linearly with the number of vehicles. Video demonstrations can be found online at \url{https://youtu.be/WYAKFMNnQfs}

In-depth research into rural road crashes

This report was produced under an agreement between Transport SA and the Road Accident Research Unit formed in the late 1990s. Due to various delays in the publication of this report, Transport SA has since become the Department for Transport, Energy and Infrastructure and the Road Accident Research Unit has become the Centre for Automotive Safety Research. The report describes a series of 236 rural road crashes investigated between 1 March 1998 and 29 February 2000 in South Australia. Investigations began with immediate attendance at the scene of the crash. The information collected for each crash included: photographs of the crash scene and vehicles involved, video record of the crash scene and vehicles in selected cases, examination of the road environment, a site plan of the crash scene and vehicle movements in the crash, examination and measurements of the vehicles involved, interviews with crash participants, interviews with witnesses, interviews with police, information on the official police report, information from Coroner’s reports, and injury data for the injured crash participants. The report provides an overall statistical summary of the sample of crashes investigated, followed by a detailed examination of the road infrastructure issues contributing to the crashes. This is done on the basis of crash type, with separate sections concerned with single vehicle crashes, midblock crashes and crashes at intersections. A section is also provided that examines the role of roadside hazards in the crashes.Baldock MRJ, Kloeden CN and McLean A